RAG and Agentic AI with LangChain, LangGraph, and LangSmith

As AI applications transition from simple chat interfaces to sophisticated enterprise tools, the ability to connect large language models to external data is crucial. This text-based course guides you through the process of designing and building robust Retrieval-Augmented Generation (RAG) systems that deliver accurate, context-aware answers. You will start with the core concepts of data ingestion, embeddings, and vector databases before moving on to advanced retrieval strategies. By exploring modern orchestration tools, you will transition from simple query-response pipelines to complex, autonomous multi-agent architectures that can plan, reflect, and correct their own mistakes. What you'll learn: - Understand the foundational architecture of RAG, including document chunking, embedding generation, and vector database storage. - Implement advanced retrieval techniques such as hybrid search, reranking, and multimodal RAG to improve response accuracy. - Build stateful, multi-agent AI workflows using LangGraph to enable autonomous decision-making and planning. - Configure evaluation, debugging, and performance tracking pipelines with LangSmith to monitor your system in production. - Apply self-correction and adaptive routing patterns so your AI agents can validate their own sources and reasoning. The course begins with essential terminology and the basics of semantic search, progressing systematically through hands-on code examples to advanced agentic workflows. You will read clear explanations, analyze production-ready code snippets, and complete written exercises to solidify your understanding. This course is designed for software developers, data practitioners, and AI enthusiasts who want to build production-grade AI applications. Basic familiarity with Python is helpful, but no prior experience with LangChain, LangGraph, or RAG is required. Start reading today to master the next generation of context-aware AI systems.